Tweeting the right to the city: digital protest and resistance surrounding the Airbnb effect

Tamajón, Lluís Garay; Morales, Soledad García; Wilson, Julie

doi:10.1080/15022250.2020.1772867

Cited by 22 publications

(14 citation statements)

References 32 publications

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“…Regarding other accommodations, P2P lodging has the particularity that the close host-guest relationship influences the image perceived by visitors [91], and the lengths of stay in Barcelona are longer [86]. Much public opinion held that Airbnb contributed to the touristification and gentrification of Barcelona [92] and to inequalities between its neighborhoods [93]. Generally, the content of Airbnb OTRs focuses on assessing the relationship with the host and the accommodation features and amenities.…”

Section: Methodsmentioning

confidence: 99%

Measuring Online Destination Image, Satisfaction, and Loyalty: Evidence from Barcelona Districts

Mariné-Roig

2021

Tourism and Hospitality

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The relationships between destination image and tourist satisfaction and loyalty have been studied extensively through surveys. This study aims to measure these constructs through big data analytics by going one step further in a line of research undertaken 8 years ago. The data source is content generated by travelers and shared on social media regarding the 10 districts of the city of Barcelona (Catalonia): more than 750,000 online travel reviews (OTRs) hosted on the Airbnb platform. This study also explores a relationship demonstrated by numerous researchers through surveys: the impact of destination image on tourist loyalty through satisfaction. However, the results are not satisfactory due to the great weight of the lodging price variable that unbalances the relationship. For example, the first district in the ranking of cognitive image categories is also the first in the ranking of average scores and of positive feelings and moods. However, the last two districts in the ranking of cognitive categories are the first in the rankings of satisfaction, positive recommendations, and cheaper prices. Additionally, the findings show that the location of the accommodation significantly determines the theme of the OTR narrative. Moreover, the results confirm previous studies on the exaggerated positivity of peer-to-peer accommodation scores: only 0.92% of 15,625 rated properties had negative overall scores.

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Section: Methodsmentioning

confidence: 99%

Measuring Online Destination Image, Satisfaction, and Loyalty: Evidence from Barcelona Districts

Mariné-Roig

2021

Tourism and Hospitality

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“…A report says that 70% of guests are fearful to stay at an Airbnb compared to hotels that have more strict and standardized cleaning protocols if they must travel during the pandemic. For the hosts, the cost of accepting guests also increases because of the stricter hygiene procedures [4]. As a result, the hosts and Airbnb itself are now facing new challenges both from inside and outside.…”

Section: Introductionmentioning

confidence: 99%

Use Python Data Analysis to Gain Insights from Airbnb Hosts

Tian

2021

Advances in Mathematical Physics

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This paper uses Python and its external data processing package to conduct an in-depth analysis machine study of Airbnb review data. Increasingly, travelers are now using Airbnb instead of staying in traditional hotels. However, in such a growing and competitive Airbnb market, many hosts may find it difficult to make their listings attractive among the many. With the development of data science, the author can now analyse large amounts of data to obtain compelling evidence that helps Airbnb hosts find certain patterns in some popular properties. By learning and emulating these patterns, many hosts may be able to increase the popularity of their properties. By using Python to analyse all data from all aspects of Airbnb listings, the author proposes to test and find correlations between certain variables and popular listings. To ensure that the results are representative and general, the author used a database containing many multidimensional details and information about Airbnb listings to date. To obtain the desired results, the author uses the Pandas, NLTK, and matplotlib packages to better process and visualize the data. Finally, the author will make some recommendations to Airbnb hosts based on the evidence generated from the data in many ways. In the future, the author will build on this to further optimize the design.

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“…Airbnb, a peer-to-peer hosting service provider and a popular sharing economy platform, has a significant impact on the global tourism and hospitality industry (Casado-Diaz et al, 2020;Dogru et al, 2020) and in Nordic countries (Adamiak, 2018(Adamiak, , 2020 including housing access, affordability issues and residential displacement (Garay et al, 2020). For example, Miami Beach, Florida surpassed all listings worldwide (Inman, 2020), while Copenhagen was among the top Nordic cities with the highest number of Airbnb listings (Adamiak, 2018) that generated an estimated 650 million dollars in 2018 alone (Airbnb, 2019).…”

Section: Introductionmentioning

confidence: 99%

Interactive value formation: drivers and outcomes from Airbnb guests’ perspectives

Sthapit

Björk

2020

Scandinavian Journal of Hospitality and Tourism

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This study explores interactive value formation, particularly the underlying drivers of three value outcomes in the Airbnb context: co-creation, co-destruction and co-recovery. The study focuses on reviews posted online by Airbnb guests in English. These posts contained customers' positive and negative experiences with Airbnb on Trustpilot. The data analysis uncovered two main themes that reflected the drivers of value co-creation, codestruction and co-recovery (company's customer service and hosts' actions). First, after a service failure, many guests experienced value co-destruction because they felt that Airbnb's customer service agents could not solve their problems in a timely and proper manner, while the use of successful recovery efforts by the service agents served as an antidote to value codestruction, thereby contributing to value co-recovery. Second, host's friendly behaviour, including prompt communication between the host and the guest, led to value co-creation. On the contrary, inadequate communication and unethical actions by the host generated value co-destruction among the guests and resulted in a decline in their well-being. The findings suggest that particular value dimensions can individually act as a source of either value co-creation or co-recovery, while their inadequate integration in the interactive value formation processes leads to value co-destruction.

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Tweeting the right to the city: digital protest and resistance surrounding the Airbnb effect

Cited by 22 publications

References 32 publications

Measuring Online Destination Image, Satisfaction, and Loyalty: Evidence from Barcelona Districts

Measuring Online Destination Image, Satisfaction, and Loyalty: Evidence from Barcelona Districts

Use Python Data Analysis to Gain Insights from Airbnb Hosts

Interactive value formation: drivers and outcomes from Airbnb guests’ perspectives

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